✍️Your Work, Their Training Fuel?

👋 Welcome! What if your ideas still mattered in a world shaped by algorithms? This issue looks at data, ownership, and creativity—reminding us that even in an AI-driven future, there’s still room for fairness, respect, and human imagination at the heart of progress!

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Apple Builds In-House AI Answer Engine – According to Bloomberg reporting, Apple has formed a new “Answers, Knowledge and Information” team to develop an AI-driven “answer engine” capable of crawling the web for general-knowledge queries, potentially integrating with Siri, Spotlight, and Safari. The project marks Apple’s first major move into standalone chatbot development after earlier setbacks in AI innovation and signals intensifying competition in the chatbot race.

Inside OpenAI’s Agents Research Program – OpenAI’s Push for Smarter AI Agents – New reporting reveals how the internal MathGen team, known for training models to solve high school math competitions, played a pivotal role in developing the o1 reasoning model. These breakthroughs are now central to OpenAI’s broader goal: building versatile AI agents that can autonomously tackle complex, multi-step computer tasks.

OpenAI Teases Next-Gen GPT-5 Release – OpenAI CEO Sam Altman teased GPT-5 in an X post, hinting at advanced chain-of-thought reasoning, reduced hallucination rates, faster inference speeds, and optimized memory footprints designed to improve accessibility across mobile and web platforms.

Perplexity Faces Web Scraping Backlash – Perplexity AI is under fire after being accused of scraping thousands of websites that explicitly blocked AI crawlers, igniting debates over data ownership, copyright enforcement, and ethical AI training practices.

Human Rights Risks Meet AI Analytics – A recent analysis explored how AI-powered tools are being deployed by corporations to flag potential labor abuses, discriminatory practices, and privacy threats across global supply chains, leveraging machine learning algorithms trained on public disclosures and satellite imagery. This trend signals a growing role for AI in corporate governance and ethical compliance.

Musk’s Pay Package Sparks AI Talent War – Tesla approved a $29 billion award for Elon Musk as the company vies to attract AI engineering talent, marking a record CEO compensation and underscoring the industry’s fierce competition for top AI expertise.

AI Interviewer Halts Job Hopes – Employers increasingly deploy AI agents to conduct and score interviews, with 96% of U.S. recruiters using them for resume screening and assessment. Experts warn bias and alienation could erode hiring fairness and candidate experience.

McDonald’s Doubles Down on AI Investment – McDonald’s plans to ramp up its artificial intelligence spending through 2027 to boost customer engagement and streamline back-office operations. A global rollout is underway, with tools for order verification, dynamic pricing, and sales forecasting at the core. Executives say the focus will be on tech infrastructure—not headcount—as the company pursues scalable automation. The strategy reflects how global firms are reallocating capital toward digital efficiency while expanding in high-skill, cost-effective markets like India.

Global M&A Surges on AI Deals – Global mergers and acquisitions have reached a record $2.6 trillion year-to-date, driven by AI-related transactions such as SoftBank-led funding for OpenAI and major tech acquisitions in cybersecurity and cloud, with deal value up 28 percent despite a 16 percent drop in deal count. Analysts attribute the surge to corporate efforts to secure AI capabilities and navigate geopolitical uncertainties through strategic acquisitions.

Markets Rally on Rate Cut Hopes – Global stocks rose Monday after a weaker-than-expected U.S. jobs report led investors to believe the Federal Reserve may cut interest rates sooner. With recent job data now under scrutiny—and political pressure mounting—markets are betting there’s an 85% chance the Fed lowers rates in September.

Conversion Raises $28M Series A – Conversion, a marketing automation platform, raised $28 million in Series A funding led by Abstract and True Ventures, boosting capital to $30 million and fueling expansion of its AI-driven workflow suite for enterprise marketing teams, with participation from Cantech investors and top angel backers.

Political Figures Back SPAC IPO – New America Acquisition I Corp, backed by Eric Trump and Donald Trump Jr., filed for a $300 million U.S. IPO to target manufacturing deals in advanced materials and automation markets, signaling political figures’ entry into the SPAC space for specialized industrial carve-outs.

With AI devouring the web, the lines between data ownership and public domain have blurred—what’s left for creators to control, and at what cost?

As AI engines gulp down more of the internet every day, the debate over who owns what—and whether permission matters—is more urgent than ever. In the past fortnight, major players like Google, Microsoft, and Elon Musk’s xAI have committed to Europe’s voluntary AI Code of Practice, which includes pledges to publish training‑data summaries and respect EU copyright laws. This marks a shift, acknowledging that “owning” the public web may no longer be an arms‑race to scrape everything.

Supporters say this is a welcome awakening: creators, publishers, and even ordinary people increasingly push back when companies track, scrape, or train on data they did not agree to give. Just six days ago, U.S. airline groups united in opposing federal limits on facial‑recognition at airports—arguing that privacy, not just copyright, must be protected. It’s a reminder that data ownership isn’t only about text and images—it’s also about consent and identity.

But criticism is loud too. Google itself warned that too much emphasis on transparency may “chill innovation”, expose trade secrets, and slow AI development—even as it signed the Code. And Meta, notably, still refuses to join the process. The message? When legal obligations collide with competitive advantage, ethical steps risk becoming marketing gestures.

Ethically speaking, the notion that “fair use” entitles big tech to anything it can scrape is unraveling. New laws in places like Brazil, India, and California—not to mention Europe’s AI Act—are demanding lists, opt‑outs, and even compensation for creators whose works train new models. AI companies must now answer not only to engineers and investors but also to lawyers, artists, and everyday users asking: who benefitted, and who got nothing?

So where do we go from here? If data really is the fuel of the next industrial revolution, should creators be paid? Should platforms set marketplaces for licensed data, as some historians and AI ethicists propose? And how do we recalibrate expectations: that public web pages do not equal public domain, and that “data ownership” may take the shape of enforced transparency more than property law?

By calling these questions out loud, we can shift the conversation from “how much AI can take” to “what should AI be allowed to take—and whose job is it to say so?”

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35,000

Big Macs — that’s the jaw-dropping lifetime total consumed by Donald Gorske, who holds the world record for the most Big Macs eaten by a single person.